Apache Kafka is an open-source distributed event-streaming platform designed for high throughput, scalability, and fault tolerance. It is widely used for real-time data pipelines and streaming applications.

1. Use Cases:

Real-time Data Ingestion: Collects real-time data from sensors, logs, etc.
Event-Driven Architecture: Captures and processes events in real-time.
Real-time Analytics: Enables immediate decision-making and insights.
Log Aggregation: Aggregates log data for monitoring.
Stream Processing: Real-time data transformations with Kafka Streams.
Messaging Systems: Decouples microservices for asynchronous communication.

2. Architecture:

Producer: Sends data to Kafka topics.
Consumer: Reads and processes data from Kafka topics.
Broker: Manages and stores data across nodes.
Topic: Data category for producers and consumers.
Partition: Divides topics for parallel processing.
Zookeeper: Coordinates broker activities.
Consumer Group: Shares message consumption across multiple consumers.

3. When to Use Kafka:

Real-Time Data Processing: Perfect for real-time analytics and fraud detection.
High-Volume Data: Handles millions of events per second.
Fault-Tolerant Systems: Offers data replication and automatic recovery.
Scalability: Scales horizontally by adding brokers.
Decoupling Systems: Ideal for microservices architectures.

4. Kafka vs Other Systems:

RabbitMQ: Kafka excels in high-throughput scenarios, while RabbitMQ is suited for complex routing and transactional applications.
ActiveMQ: Kafka provides better scalability and real-time processing; ActiveMQ is for traditional message queuing.
Apache Pulsar: Pulsar offers better scalability and geo-replication, but Kafka performs better in high-throughput cases.

5. Key Features:

High Throughput: Handles millions of events per second.
Scalable: Horizontally scalable by adding brokers.
Fault Tolerant: Data availability during failures.
Low Latency: Ideal for real-time analytics.
Durability: Persistent data even if not yet processed.
Stream Processing: Integrated with Kafka Streams and Apache Flink.

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